refactor and run on all plugins

Author: dungscout96

.gitignore

@@ -2,3 +2,4 @@ _site/
 .jekyll-cache/
 _config-claire.yml
 Gemfile.lock
+code/plugins/github/

code/SIFT.py

@@ -0,0 +1,34 @@
+import os
+import sys
+import shutil
+
+# open a text file ending with .md and append a paragraph to it
+def reformat_plugin(dirpath, plugin_name):
+    plugins_dir = '/Users/dtyoung/Documents/EEGLAB/sccn.github.io/plugins'
+    index_file = os.path.join(plugins_dir, '{plugin_name}.md'.format(plugin_name=plugin_name))
+    shutil.copyfile(os.path.join(dirpath, 'README.md'), index_file)
+    with open(index_file) as f:
+        text = f.read()
+        append_text = '''---
+layout: default
+title: {plugin_name}
+long_title: {plugin_name}
+parent: Plugins
+---
+'''.format(plugin_name=plugin_name)
+        text = append_text + text
+        with open(index_file, 'w') as out:
+            out.write(text)
+
+
+# main
+def main():
+    if len(sys.argv) != 3:
+        print('Usage: python README_plugin.py <plugin_dir_path> <plugin_name>')
+        sys.exit(1)
+    dirpath = sys.argv[1]
+    plugin_name = sys.argv[2]
+    reformat_plugin(dirpath, plugin_name)
+
+if __name__ == "__main__":
+    main()

code/plugins/README_plugin.py

@@ -0,0 +1,63 @@
+import os
+import sys
+import shutil
+
+# open a text file ending with .md and append a paragraph to it
+# Usage: python test.py <filename>.md
+def append_to_file(filepath, filename, parent, output_file):
+    with open(filepath) as f:
+        text = f.read()
+        append_text = '''---
+layout: default
+title: {filename}
+long_title: {filename}
+parent: {parent}
+grand_parent: Plugins
+---
+'''.format(filename=filename, parent=parent)
+        text = append_text + text
+    with open(output_file, 'w') as out:
+        out.write(text)
+
+def reformat_plugin_dir(plugin_input_dir, plugin_name):
+    # plugins_output_dir = '/Users/dtyoung/Documents/EEGLAB/sccn.github.io/plugins'
+    plugin_output_dir = os.path.join('/Users/dtyoung/Documents/EEGLAB/sccn.github.io/plugins', plugin_name)
+    if not os.path.exists(plugin_output_dir):
+        os.makedirs(plugin_output_dir)
+    # copy image directory from input to output dir
+    if os.path.exists(os.path.join(plugin_input_dir, 'images')):
+        shutil.copytree(os.path.join(plugin_input_dir, 'images'), os.path.join(plugin_output_dir, 'images'))
+
+    index_file = os.path.join(plugin_output_dir, 'index.md')
+    shutil.copyfile(os.path.join(plugin_input_dir, 'Home.md'), index_file)
+    with open(index_file) as f:
+        text = f.read()
+        append_text = '''---
+layout: default
+title: {plugin_name}
+long_title: {plugin_name}
+parent: Plugins
+categories: plugins
+has_children: true
+---
+'''.format(plugin_name=plugin_name)
+        text = append_text + text
+        with open(index_file, 'w') as out:
+            out.write(text)
+
+    for root, dirs, files in os.walk(plugin_input_dir):
+        for file in files:
+            if file.endswith('.md') and not file.startswith('index') and not file.startswith('Home'):
+                append_to_file(os.path.join(plugin_input_dir, file), file.strip('.md'), plugin_name, os.path.join(plugin_output_dir, file))
+
+# main
+def main():
+    if len(sys.argv) != 3:
+        print('Usage: python test.py <plugin_dir_path> <plugin_name>')
+        sys.exit(1)
+    dirpath = sys.argv[1]
+    plugin_name = sys.argv[2]
+    reformat_plugin_dir(dirpath, plugin_name)
+
+if __name__ == "__main__":
+    main()

code/plugins/Wiki_plugin.py

@@ -0,0 +1,36 @@
+import os
+import subprocess
+
+def run_command(command):
+    result = subprocess.run(command, shell=True, capture_output=False, text=True)
+    if result.returncode != 0:
+        print(f"Command failed: {command}\nError: {result.stderr}")
+    return result
+
+def update_repo(repo, script, type='readme'):
+    current_dir = os.getcwd()
+    repo_path = os.path.join(current_dir, 'github', repo)
+    
+    if os.path.exists(repo_path):
+        os.chdir(repo_path)
+        run_command('git pull')
+    else:
+        if type == "wiki":
+            run_command(f'git clone https://github.com/sccn/{repo}.wiki.git {repo_path}')
+        else:
+            run_command(f'git clone https://github.com/sccn/{repo}.git {repo_path}')
+        
+    os.chdir(current_dir)
+    run_command(f'python {script} {repo_path} {repo}')
+
+if __name__ == "__main__":
+    # if 'github' not in current directory, create it
+    if not os.path.exists('github'):
+        os.makedirs('github')
+
+    wiki_plugins = ['SIFT', 'get_chanlocs', 'NFT', 'PACT', 'nsgportal', 'clean_rawdata']
+    for plugin in wiki_plugins:
+        update_repo(plugin, "Wiki_plugin.py", 'wiki')
+    readme_plugins = ['roiconnect', 'EEG-BIDS', 'trimOutlier', 'groupSIFT', 'nwbio', 'ICLabel', 'dipfit', 'eegstats', 'PowPowCAT', 'PACTools', 'zapline-plus', 'amica', 'fMRIb', 'relica', 'std_dipoleDensity', 'imat', 'viewprops', 'cleanline', 'ARfitStudio ', 'NIMA', 'firfilt']
+    # for plugin in readme_plugins:
+    #     update_repo(plugin, "README_plugin.py")

code/plugins/update_plugins.py

@@ -0,0 +1,16 @@
+current_dir=$(pwd)
+
+repo="SIFT"
+if cd ../../$repo; then git pull; else git clone https://github.com/sccn/$repo.wiki.git ../../$repo; fi
+python Wiki_plugin.py ../../$repo $repo
+cd $current_dir
+
+repo="EEG-BIDS"
+if cd ../../$repo; then git pull; else git clone https://github.com/sccn/$repo.git ../../$repo; fi
+python README_plugin.py ../../$repo $repo
+cd $current_dir
+
+repo="roiconnect"
+if cd ../../$repo; then git pull; else git clone https://github.com/sccn/$repo.git ../../$repo; fi
+python README_plugin.py ../../$repo $repo
+cd $current_dir

code/plugins/update_plugins.sh

@@ -0,0 +1,137 @@
+---
+layout: default
+title: ARfitStudio
+long_title: ARfitStudio
+parent: Plugins
+---
+What is ARfitStudio
+-------------------
+
+First of all, ARfit is a collection of Matlab modules developed by Tapio
+Schneider and Arnold Neumaier for estimating parameters of multivariate
+autoregressive (AR) models, diagnostic checking of fitted AR models, and
+analyzing eigenmodes of fitted AR models. This plugin, ARfitStudio, uses
+it so that one can interactively clean TMS-induced short-burst (5-10 ms)
+artifacts and so on. The nature of the artifact does not matter (could
+be spikes by artifact, could be zeros, NaNs, etc), as long as the
+artifact duration is short and they are correctly marked. It has
+strength on the following points.
+
+-   To perform quick (e.g. smart epoching & integration to continuous
+    data using mcolon(); before and after correction comparison with one
+    click, etc) and intuitive (e.g. training, correction, and blending
+    windows overlaid on grand-median ERPs) correction of spiky
+    artifacts.
+-   Immediately usable after importing the continuous data; it is the
+    very first stage of the preprocess pipeline.
+
+This plugin is dependent on ARfit and mcolon. ARfit can be installed by
+installing SIFT from EEGLAB plugin manager. To use mcolon(), users
+should compile the mex file.
+
+<http://www.mathworks.com/matlabcentral/fileexchange/174-arfit>
+<http://www.mathworks.com/matlabcentral/fileexchange/29854-multiple-colon/content/mcolonFolder/mcolon.m>
+
+Why ARfitStudio
+---------------
+
+If there are spike artifacts with high amplitude, we cannot filter the
+data (because spikes spread in the time domain). In certain situation,
+it is more important to make them harmless for the sake of data process,
+rather than recovering the underlying signals (the latter is often
+impossible). This plugin provides a solution for it.
+
+How it works
+------------
+
+It learns autoregressive model from the training period (green), which
+is the past of the spike, to make a prediction to replace problematic
+spike data points (red). For smooth connection, one can also set
+optional blending period (light magenta) during which predicted signal
+and original signals are gradually mixed with a linear slope. Thus,
+*this plugin replaces the bad data points using the past information*
+(therefore temporal interpolation). It does NOT recover the signal under
+the noise. One can select multiple markers for correction. In the main
+plot, grand-median ERPs for all the channels are shown. To save the
+result, just save the EEG dataset (do not check 'restore the last data'
+for this!)
+
+Screenshots
+-----------
+
+![300px\|Figure 1. interpolateSpike:
+GUI](images/Screenshot3_interpolatespike.png)
+
+![600px\|Figure 2. interpolateSpike: Before
+correction](images/Screenshot2_interpolatespike.png)
+
+![600px\|Figure 3. interpolateSpike: After
+correction](images/Screenshot1_interpolatespike.png)
+
+Data by courtesy of Michael Borich
+
+If you want to use ICA for the final analysis
+---------------------------------------------
+
+Because this ARfit-based interpolation is performed for the same time
+points across channels, ICA cannot decompose this period. As a result,
+you'll see strange spikes in IC activations (i.e., EEG.icaact) during
+the correction windows. In the plots below, I show 1) ERP of IC
+activation for 33ch with ARfitStudio applied on channels (left), 2) the
+same but ARfitStudio applied on ICs (right). The noise was originally
+present within -/+10 ms relative to latency zero.
+
+![Onchannels.png](images/Onchannels.png)
+![Onics.png](images/Onics.png)
+
+Performing ARfit-interpolation for ICA properly is complicated.
+
+1.  Import data.
+2.  Perform ARfitStudio on channels.
+3.  Preprocess the data (high-pass filter, CleanLine plugin,
+    clean_rawdata plugin, average referencing, AMICA plugin, DIPFIT
+    plugin, fitTwoDipoles plugin). Note that you need to exclude the
+    interpolated data from ICA process (those have no multivariate
+    property across channels, so not decomposable).
+4.  Perform ARfitStudio on IC activations.
+5.  Backproject interpolated IC activation to reconstruct channel data.
+
+Note that the ARfitStudio is used twice, and the first application is
+purposed only for various filters, not even for ICA. In this way, you
+can obtain both clean channel signals and IC activations. For detail,
+see 'batchDemoForICA.m' included in the download package. I recommend
+you take a look because there are several cumbersome steps you have to
+go through.
+
+Note for batch users (03/11/2019 updated)
+-----------------------------------------
+
+If one wants to run the process as a batch,
+
+1.  Use EEGLAB function pop_epoch() to crop out peri-event data points.
+    Note all the negative latencies will be used as a learning period
+    for ARfit.
+2.  Use arfit2interpolate(). Note that the last input
+    'last_n_pointsToBlend' does NOT specify the additional length to
+    the data points to the main interpolation window (as the GUI
+    operation indicates), but it actually specifies the last data points
+    of the main interpolation window. For example, outputData =
+    arfit2interpolate(EEG.data, \[80 95\], 5) means, \[80:90\] for 100%
+    interpolation, and \[91:95\] is blended with \[83% 67% 50% 33% 17%\]
+    interpolated data blended with \[17% 33% 50% 67% 83%\] of the
+    original data, respectively.
+3.  Use putBackEpoch2Continuous() to re-construct the corrected, epoched
+    data to the continuous data.
+
+Reference
+---------
+
+A. Neumaier and T. Schneider, 2001: Estimation of parameters and
+eigenmodes of multivariate autoregressive models. ACM Trans. Math.
+Softw., 27, 27-57.
+
+T. Schneider and A. Neumaier, 2001: Algorithm 808: ARfit – A Matlab
+package for the estimation of parameters and eigenmodes of multivariate
+autoregressive models. ACM Trans. Math. Softw., 27, 58-65.
+
+Authors: Makoto Miyakoshi and Tim Mullen. SCCN, INC, UCSD